AI Governance and Model Evaluation Services

Model evaluation measures how an AI system performs against defined tasks and risks. AI governance establishes the ownership, rules, controls, documentation, and monitoring used to manage that system. Evaluation produces evidence. Governance determines how the evidence affects approval, deployment, access, change, and ongoing operation. Effective AI programs need both.

Yes. We can assess an internally developed system, a third-party platform, an existing proof of concept, or an AI feature already in production. The depth of the evaluation depends on the access available. Source code and architecture access allow a deeper technical review, but useful behavioral and workflow testing can often begin through APIs, staging environments, system documentation, and representative user access.

Not always. For behavioral evaluation, we may only need access to the application, API, representative data, expected outputs, and existing documentation. Source-code, infrastructure, prompt, and retrieval access may be required when the engagement includes architecture assessment, remediation, security controls, or implementation.

We use task-specific questions with known source material and expected outcomes. We assess whether the system retrieves the appropriate evidence, represents that evidence accurately, identifies uncertainty, and refuses to invent an answer when support is unavailable. Because hallucination risk cannot be universally eliminated, we combine evaluation with source controls, citation checks, refusal behavior, human escalation, and ongoing monitoring.

Material changes should trigger regression testing. We version the relevant models, prompts, rules, tools, and knowledge sources so the updated system can be evaluated against the previously approved test set. Release criteria, staged rollout, monitoring, and rollback procedures are defined according to the system’s risk.

Yes. Agent evaluations include both what the system says and what it attempts to do. We test tool selection, permissions, action parameters, approval requirements, failure recovery, data access, logging, and the final effect on connected systems.

Yes. We can map evaluation activities, responsibilities, controls, documentation, and ongoing processes to relevant NIST AI RMF and Generative AI Profile outcomes. National Institute of Standards and Technology alignment is used as a practical reference structure. It is not a certification, legal opinion, or guarantee of compliance.

No. OST provides technical evaluation, implementation, and governance support. We work with your legal, privacy, security, accessibility, and procurement teams to translate applicable requirements into technical and operational controls. Final legal interpretations and formal compliance determinations remain with qualified counsel and the appropriate assurance professionals.

It can begin as a focused assessment, but evaluation should continue when a model, prompt, knowledge source, workflow, integration, or risk profile materially changes. We can deliver a one-time baseline, implement a recurring internal evaluation process, or provide ongoing monitoring and governance support.